Reference: https://rdrr.io/cran/TSMining/ https://rpubs.com/esobolewska/dtw-time-series https://cran.r-project.org/web/packages/STMotif/vignettes/STMotif.html https://cran.r-project.org/web/packages/tsmp/vignettes/press.html
The data was downloaded from the author source. Then imported in R as a excel file.
library(readxl)
Table_S1_DEgen <- read_excel("D:/ALDA 2021/CARLETON 2022/STAT5900-Genomic/Final Project STAT5900F/Table S1 Rev1- Differentially expressed gene during encystation.xlsx", sheet = "log2 fold change")
#View(Table_S1_DEgen)
head(Table_S1_DEgen)
NA
Gen_T1_T2<-Table_S1_DEgen[,-3] #save gene_id and time from T1 to T2
head(Gen_T1_T2)
Gen_T1_T2$Geneid=as.factor(Gen_T1_T2$Geneid)
Gen_T1_T2$T1 =as.numeric(Gen_T1_T2$T1)
mean(as.matrix(Gen_T1_T2[1,-c(1,2)],na.rm = T))# try for the first row
[1] 2.773333
sd(as.matrix(Gen_T1_T2[1,-c(1,2)],na.rm = T)) # try for the first row
[1] 2.84546
# this function will delete all rows with missing data based on a threshold n (n%)
delete.row_na <- function(D, n) {
D[rowSums(D==0)<= n*ncol(D),]
}
Remove observations with missing values more than 95% (rows removed)
gen_T1_T2_del<-delete.row_na(Gen_T1_T2[,-c(1,2)],0.95)
dim(gen_T1_T2_del)
[1] 3908 12
i_names<-rownames(gen_T1_T2_del)# names of genes saved after removing those with NA
length(i_names)
[1] 3908
head(i_names)
[1] "1" "2" "3" "4" "5" "6"
Each row is considered as a time series (observation). For a better similarity measure we will standardize the time series (each row of the dataset).
ByRow_Sd=function(M){
A=matrix(NA,nrow=nrow(M),ncol=ncol(M))
for(i in 1:nrow(M))
{
for (j in 1:ncol(M))
{
A[i,j]=(M[i,j]-mean(M[i,],na.rm=T))/sd(M[i,],na.rm = T)
}
}
return(A)
}
# transform our dataframe as a matrix to work better with the standardization function by row (removed the geneID column)
Gen_1<-as.matrix(gen_T1_T2_del[,-1])
Gen_standardized<-ByRow_Sd(Gen_1)
# checking for the first row the mean and sd
mean(Gen_standardized[1,],na.rm = T)
[1] 5.043511e-17
sd(Gen_standardized[1,],na.rm=T)
[1] 1
(after log2 transformed taken from the authors)
# Graph before standardization
Plot_gene_real=function(M){
A=plot(M[1,],ylim=c(-5,10),xlab="Time",ylab="value",main="Gene expression over time-before",type="l")
for (i in 1:nrow(M)){
lines(M[i,],col="darkblue")
}
print(A)
}
# before removing T1 which is 0 for all genes
Plot_gene_real(Gen_1)
NULL
abline(v=4,col="red",lwd=2)
abline(v=7,col="purple",lwd=2)
abline(v=11,col="green",lwd=2)
arrows(10,-5,11,-3.5, col="green",lwd=2)
arrows(10,10,11,9.5,col="green",lwd=2)
text(5,-3,"time 10.5h",col="red")
text(5.8,10,"time 21h",col="purple")
text(9.6,-4.5,"Cyst",col="green")
# after removing T1 which is 0 for all genes
Plot_gene_real(Gen_1)
NULL
abline(v=4,col="red",lwd=2)
abline(v=7,col="purple",lwd=2)
abline(v=10,col="green",lwd=2)
arrows(9,-5,10,-3.5, col="green",lwd=2)
arrows(9,10,10,9.5,col="green",lwd=2)
text(5,-3,"time 10.5h",col="red")
text(5.8,10,"time 21h",col="purple")
text(9.6,-4.5,"Cyst",col="green")
Since they all start at time 0 in moment of time T1 we may remove this time from our consideration!
Plot_gene_real(Gen_1)
NULL
Plot_gene=function(M){
A=plot(M[1,],ylim=c(-3.5,3.5),xlab="Time",ylab="value",main="Gene expression over time-after")
for (i in 1:nrow(M)){
lines(M[i,],col="darkblue")
}
print(A)
}
Plot_gene(Gen_standardized)
NULL
abline(v=4,col="red",lwd=2)
abline(v=7,col="purple",lwd=2)
abline(v=10,col="green",lwd=2)
arrows(8,4.5,10,3.1, col="green",lwd=2)
arrows(8,-4.5,10,-3.1,col="green",lwd=2)
text(5,-3,"time 10.5h",col="red")
text(5.8,3,"time 21h",col="purple")
text(9.6,-3,"Cyst",col="green")
library(data.table)
Registered S3 method overwritten by 'data.table':
method from
print.data.table
data.table 1.14.4 using 4 threads (see ?getDTthreads). Latest news: r-datatable.com
library(dplyr)
Attaching package: ‘dplyr’
The following objects are masked from ‘package:data.table’:
between, first, last
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union
# transpose function
Gen_ID<-Gen_T1_T2$Geneid # save geneID information for latter
gen_T1_T2_transp<-t(Gen_standardized) # standardized time series
dim(gen_T1_T2_transp)
[1] 11 3908
# in the transpose matrix genes are in columns and time in rows, 12x5320
library(tidyr)
library(tidyverse)
Registered S3 methods overwritten by 'dbplyr':
method from
print.tbl_lazy
print.tbl_sql
── Attaching packages ──────────────────────── tidyverse 1.3.2 ──✔ ggplot2 3.4.0 ✔ purrr 0.3.5
✔ tibble 3.1.8 ✔ stringr 1.4.1
✔ readr 2.1.3 ✔ forcats 0.5.2── Conflicts ─────────────────────────── tidyverse_conflicts() ──
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✖ purrr::transpose() masks data.table::transpose()
gene_long<-gather(data.frame(gen_T1_T2_transp[c(1:11),c(1:3908)]))
head(gene_long)
dim(gene_long)
[1] 42988 2
gene_long$value=as.numeric(gene_long$value)
# make a timepoint column
t<-c("3.5h","7h","10.5h","14h","17.5h","21h","24.5h","28h","31.5h","Cyst","T2")
gene_long<- gene_long %>% mutate(time=rep(1:11,3908))
View(gene_long)
# plot by key first 12 genes
gene_long[1:220,] %>%
ggplot(aes(x=time, y=value, color=key)) +
geom_line( size=1) +
ggtitle("Gene sequences over time") +
theme(legend.position="none")+
facet_wrap(~ key , scales = "free", nrow= 5)
Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
Please use `linewidth` instead.
gene_long[1:44,] %>%
ggplot(aes(x=time, y=value, color=key)) +
geom_line( size=1) +
ggtitle("Gene sequences over time") +
theme(legend.position="none")+
facet_wrap(~ key , scales = "free", nrow= 1)
NA
# plot by key first 12 genes
t1<-c("3.5h","7h","10.5h","14h","17.5","21h","24.5h","28h","31.5h","cycl","T2")
gene_long<-gene_long %>% mutate(TIME=rep(t1,3908))
gene_long %>%
ggplot(aes(x=time, y=value, color=key)) +
geom_line( size=0.1) +
ggtitle("gene evolution over time") +
theme(legend.position="none")
This will halp us use the time series methodologies. The long time series here will have a frequency of 11.
library(fpp3)
── Attaching packages ───────────────────────────── fpp3 0.4.0 ──
✔ lubridate 1.8.0 ✔ feasts 0.3.0
✔ tsibble 1.1.3 ✔ fable 0.3.2
✔ tsibbledata 0.4.1
── Conflicts ────────────────────────────────── fpp3_conflicts ──
✖ dplyr::between() masks data.table::between()
✖ lubridate::date() masks base::date()
✖ dplyr::filter() masks stats::filter()
✖ dplyr::first() masks data.table::first()
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✖ tsibble::intersect() masks base::intersect()
✖ tsibble::interval() masks lubridate::interval()
✖ lubridate::isoweek() masks data.table::isoweek()
✖ tsibble::key() masks data.table::key()
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✖ lubridate::second() masks data.table::second()
✖ tsibble::setdiff() masks base::setdiff()
✖ purrr::transpose() masks data.table::transpose()
✖ tsibble::union() masks base::union()
✖ lubridate::wday() masks data.table::wday()
✖ lubridate::week() masks data.table::week()
✖ lubridate::yday() masks data.table::yday()
✖ lubridate::year() masks data.table::year()
library(forecast)
Registered S3 method overwritten by 'quantmod':
method from
as.zoo.data.frame zoo
Attaching package: ‘forecast’
The following objects are masked from ‘package:fabletools’:
accuracy, forecast
library(fpp2)
── Attaching packages ─────────────────────────────── fpp2 2.4 ──
✔ fma 2.4 ✔ expsmooth 2.3
── Conflicts ────────────────────────────────── fpp2_conflicts ──
✖ purrr::transpose() masks data.table::transpose()
Attaching package: ‘fpp2’
The following object is masked from ‘package:fpp3’:
insurance
plot(ts(gene_long$value,start=1, frequency=11), main="long time series of observations", col="darkblue")
# ggseasonplot(ts(gene_long$value,start=2000, frequency=12),season.labels = NULL)
autoplot(ts(gene_long$value,start=1, frequency=11),col="darkblue")
library(fpp3)
library(forecast)
set.seed(12345678)
gene_ts<-ts(gene_long$value, start=1,frequency=11)
#ggseasonplot(gene_ts)
seasonplot(gene_ts, col=c("pink","orange","purple","red","grey"),main="Seasonplot gene time expression",xlab="time",season.labels=c("3.5h","7h","10.5h","14h","17.5","21h","24.5h","28h","31.5h","cycl","T2")) #ok
seasonplot(gene_ts, col=c("pink","orange","purple","red","grey"),main="Seasonplot",xlab="time",season.labels=c("3.5h","7h","10.5h","14h","17.5","21h","24.5h","28h","31.5h","cycl","T2")) #ok
#TSMP library
reference: https://github.com/matrix-profile-foundation/tsmp
library(tsmp)
Reference: https://www.r-bloggers.com/2020/09/100-time-series-data-mining-questions-part-2/
# Basic workflow:
matrix <- tsmp(gene_ts, window_size = 11) %>%
find_motif(n_motifs = 3) %T>%
plot()
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STOMP [===>---------------------------] 11% at 220 it/s, elapsed: 22s, eta: 3m
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STOMP [===>---------------------------] 14% at 227 it/s, elapsed: 27s, eta: 3m
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STOMP [====>--------------------------] 15% at 227 it/s, elapsed: 28s, eta: 3m
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Finished in 2.76 mins
matrix <- tsmp(gene_ts, window_size = 11) %>%
find_motif(n_motifs = 4) %T>%
plot()
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STOMP [==>----------------------------] 8% at 251 it/s, elapsed: 14s, eta: 3m
STOMP [==>----------------------------] 8% at 250 it/s, elapsed: 14s, eta: 3m
STOMP [==>----------------------------] 8% at 251 it/s, elapsed: 14s, eta: 3m
STOMP [==>----------------------------] 8% at 251 it/s, elapsed: 15s, eta: 3m
STOMP [==>----------------------------] 9% at 251 it/s, elapsed: 15s, eta: 3m
STOMP [==>----------------------------] 9% at 252 it/s, elapsed: 15s, eta: 3m
STOMP [==>----------------------------] 9% at 248 it/s, elapsed: 15s, eta: 3m
STOMP [==>----------------------------] 9% at 249 it/s, elapsed: 15s, eta: 3m
STOMP [==>----------------------------] 9% at 248 it/s, elapsed: 15s, eta: 3m
STOMP [==>----------------------------] 9% at 249 it/s, elapsed: 15s, eta: 3m
STOMP [==>----------------------------] 9% at 248 it/s, elapsed: 15s, eta: 3m
STOMP [==>----------------------------] 9% at 249 it/s, elapsed: 15s, eta: 3m
STOMP [==>----------------------------] 9% at 248 it/s, elapsed: 15s, eta: 3m
STOMP [==>----------------------------] 9% at 249 it/s, elapsed: 15s, eta: 3m
STOMP [==>----------------------------] 9% at 248 it/s, elapsed: 15s, eta: 3m
STOMP [==>----------------------------] 9% at 249 it/s, elapsed: 15s, eta: 3m
STOMP [==>----------------------------] 9% at 248 it/s, elapsed: 15s, eta: 3m
STOMP [==>----------------------------] 9% at 249 it/s, elapsed: 15s, eta: 3m
STOMP [==>----------------------------] 9% at 248 it/s, elapsed: 15s, eta: 3m
STOMP [==>----------------------------] 9% at 249 it/s, elapsed: 15s, eta: 3m
STOMP [==>----------------------------] 9% at 248 it/s, elapsed: 15s, eta: 3m
STOMP [==>----------------------------] 9% at 249 it/s, elapsed: 15s, eta: 3m
STOMP [==>----------------------------] 9% at 248 it/s, elapsed: 15s, eta: 3m
STOMP [==>----------------------------] 9% at 249 it/s, elapsed: 15s, eta: 3m
STOMP [==>----------------------------] 9% at 248 it/s, elapsed: 15s, eta: 3m
STOMP [==>----------------------------] 9% at 249 it/s, elapsed: 15s, eta: 3m
STOMP [==>----------------------------] 9% at 248 it/s, elapsed: 15s, eta: 3m
STOMP [==>----------------------------] 9% at 249 it/s, elapsed: 15s, eta: 3m
STOMP [==>----------------------------] 9% at 248 it/s, elapsed: 15s, eta: 3m
STOMP [==>----------------------------] 9% at 249 it/s, elapsed: 15s, eta: 3m
STOMP [==>----------------------------] 9% at 249 it/s, elapsed: 16s, eta: 3m
STOMP [==>----------------------------] 9% at 250 it/s, elapsed: 16s, eta: 3m
STOMP [==>----------------------------] 9% at 249 it/s, elapsed: 16s, eta: 3m
STOMP [==>----------------------------] 9% at 250 it/s, elapsed: 16s, eta: 3m
STOMP [==>----------------------------] 9% at 249 it/s, elapsed: 16s, eta: 3m
STOMP [==>----------------------------] 9% at 250 it/s, elapsed: 16s, eta: 3m
STOMP [==>----------------------------] 9% at 249 it/s, elapsed: 16s, eta: 3m
STOMP [==>----------------------------] 9% at 250 it/s, elapsed: 16s, eta: 3m
STOMP [==>----------------------------] 9% at 249 it/s, elapsed: 16s, eta: 3m
STOMP [==>----------------------------] 9% at 250 it/s, elapsed: 16s, eta: 3m
STOMP [==>----------------------------] 9% at 249 it/s, elapsed: 16s, eta: 3m
STOMP [==>----------------------------] 9% at 250 it/s, elapsed: 16s, eta: 3m
STOMP [==>----------------------------] 9% at 249 it/s, elapsed: 16s, eta: 3m
STOMP [==>----------------------------] 9% at 250 it/s, elapsed: 16s, eta: 3m
STOMP [==>----------------------------] 9% at 246 it/s, elapsed: 16s, eta: 3m
STOMP [==>----------------------------] 9% at 247 it/s, elapsed: 16s, eta: 3m
STOMP [==>----------------------------] 9% at 248 it/s, elapsed: 16s, eta: 3m
STOMP [==>----------------------------] 9% at 247 it/s, elapsed: 16s, eta: 3m
STOMP [==>----------------------------] 9% at 248 it/s, elapsed: 16s, eta: 3m
STOMP [==>----------------------------] 9% at 247 it/s, elapsed: 16s, eta: 3m
STOMP [==>----------------------------] 9% at 248 it/s, elapsed: 16s, eta: 3m
STOMP [==>----------------------------] 9% at 247 it/s, elapsed: 16s, eta: 3m
STOMP [==>----------------------------] 9% at 248 it/s, elapsed: 16s, eta: 3m
STOMP [==>----------------------------] 9% at 247 it/s, elapsed: 16s, eta: 3m
STOMP [==>----------------------------] 9% at 248 it/s, elapsed: 16s, eta: 3m
STOMP [==>----------------------------] 9% at 247 it/s, elapsed: 16s, eta: 3m
STOMP [==>----------------------------] 9% at 248 it/s, elapsed: 16s, eta: 3m
STOMP [==>----------------------------] 9% at 247 it/s, elapsed: 16s, eta: 3m
STOMP [==>----------------------------] 9% at 248 it/s, elapsed: 16s, eta: 3m
STOMP [==>----------------------------] 9% at 247 it/s, elapsed: 16s, eta: 3m
STOMP [==>----------------------------] 9% at 248 it/s, elapsed: 16s, eta: 3m
STOMP [==>----------------------------] 10% at 248 it/s, elapsed: 16s, eta: 3m
STOMP [==>----------------------------] 10% at 248 it/s, elapsed: 17s, eta: 3m
STOMP [==>----------------------------] 10% at 249 it/s, elapsed: 17s, eta: 3m
STOMP [==>----------------------------] 10% at 248 it/s, elapsed: 17s, eta: 3m
STOMP [==>----------------------------] 10% at 249 it/s, elapsed: 17s, eta: 3m
STOMP [==>----------------------------] 10% at 250 it/s, elapsed: 17s, eta: 3m
STOMP [==>----------------------------] 10% at 249 it/s, elapsed: 17s, eta: 3m
STOMP [==>----------------------------] 10% at 250 it/s, elapsed: 17s, eta: 3m
STOMP [==>----------------------------] 10% at 249 it/s, elapsed: 17s, eta: 3m
STOMP [==>----------------------------] 10% at 250 it/s, elapsed: 17s, eta: 3m
STOMP [==>----------------------------] 10% at 249 it/s, elapsed: 17s, eta: 3m
STOMP [==>----------------------------] 10% at 250 it/s, elapsed: 17s, eta: 3m
STOMP [==>----------------------------] 10% at 251 it/s, elapsed: 17s, eta: 3m
STOMP [==>----------------------------] 10% at 250 it/s, elapsed: 17s, eta: 3m
STOMP [==>----------------------------] 10% at 251 it/s, elapsed: 17s, eta: 3m
STOMP [==>----------------------------] 10% at 250 it/s, elapsed: 17s, eta: 3m
STOMP [==>----------------------------] 10% at 251 it/s, elapsed: 17s, eta: 3m
STOMP [==>----------------------------] 10% at 251 it/s, elapsed: 18s, eta: 3m
STOMP [==>----------------------------] 10% at 248 it/s, elapsed: 18s, eta: 3m
STOMP [==>----------------------------] 10% at 249 it/s, elapsed: 18s, eta: 3m
STOMP [==>----------------------------] 10% at 250 it/s, elapsed: 18s, eta: 3m
STOMP [==>----------------------------] 10% at 249 it/s, elapsed: 18s, eta: 3m
STOMP [==>----------------------------] 10% at 250 it/s, elapsed: 18s, eta: 3m
STOMP [==>----------------------------] 10% at 249 it/s, elapsed: 18s, eta: 3m
STOMP [==>----------------------------] 10% at 250 it/s, elapsed: 18s, eta: 3m
STOMP [==>----------------------------] 10% at 249 it/s, elapsed: 18s, eta: 3m
STOMP [==>----------------------------] 10% at 250 it/s, elapsed: 18s, eta: 3m
STOMP [==>----------------------------] 10% at 249 it/s, elapsed: 18s, eta: 3m
STOMP [==>----------------------------] 10% at 250 it/s, elapsed: 18s, eta: 3m
STOMP [==>----------------------------] 10% at 249 it/s, elapsed: 18s, eta: 3m
STOMP [==>----------------------------] 10% at 250 it/s, elapsed: 18s, eta: 3m
STOMP [==>----------------------------] 10% at 249 it/s, elapsed: 18s, eta: 3m
STOMP [==>----------------------------] 10% at 250 it/s, elapsed: 18s, eta: 3m
STOMP [==>----------------------------] 11% at 250 it/s, elapsed: 18s, eta: 3m
STOMP [==>----------------------------] 11% at 249 it/s, elapsed: 18s, eta: 3m
STOMP [==>----------------------------] 11% at 250 it/s, elapsed: 18s, eta: 3m
STOMP [==>----------------------------] 11% at 249 it/s, elapsed: 18s, eta: 3m
STOMP [==>----------------------------] 11% at 250 it/s, elapsed: 18s, eta: 3m
STOMP [==>----------------------------] 11% at 249 it/s, elapsed: 18s, eta: 3m
STOMP [==>----------------------------] 11% at 250 it/s, elapsed: 18s, eta: 3m
STOMP [==>----------------------------] 11% at 251 it/s, elapsed: 18s, eta: 3m
STOMP [==>----------------------------] 11% at 250 it/s, elapsed: 18s, eta: 3m
STOMP [==>----------------------------] 11% at 251 it/s, elapsed: 18s, eta: 3m
STOMP [==>----------------------------] 11% at 250 it/s, elapsed: 18s, eta: 3m
STOMP [==>----------------------------] 11% at 251 it/s, elapsed: 18s, eta: 3m
STOMP [==>----------------------------] 11% at 250 it/s, elapsed: 19s, eta: 3m
STOMP [==>----------------------------] 11% at 251 it/s, elapsed: 19s, eta: 3m
STOMP [==>----------------------------] 11% at 250 it/s, elapsed: 19s, eta: 3m
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STOMP [==>----------------------------] 11% at 248 it/s, elapsed: 19s, eta: 3m
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STOMP [===>---------------------------] 11% at 250 it/s, elapsed: 19s, eta: 3m
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STOMP [===>---------------------------] 12% at 252 it/s, elapsed: 20s, eta: 2m
STOMP [===>---------------------------] 12% at 253 it/s, elapsed: 20s, eta: 2m
STOMP [===>---------------------------] 12% at 252 it/s, elapsed: 20s, eta: 3m
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STOMP [===>---------------------------] 12% at 250 it/s, elapsed: 21s, eta: 3m
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STOMP [===>---------------------------] 13% at 255 it/s, elapsed: 23s, eta: 2m
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STOMP [====>--------------------------] 15% at 255 it/s, elapsed: 24s, eta: 2m
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STOMP [====>--------------------------] 15% at 256 it/s, elapsed: 25s, eta: 2m
STOMP [====>--------------------------] 15% at 255 it/s, elapsed: 25s, eta: 2m
STOMP [====>--------------------------] 15% at 256 it/s, elapsed: 25s, eta: 2m
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STOMP [====>--------------------------] 15% at 254 it/s, elapsed: 26s, eta: 2m
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STOMP [====>--------------------------] 16% at 256 it/s, elapsed: 26s, eta: 2m
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STOMP [====>--------------------------] 16% at 256 it/s, elapsed: 26s, eta: 2m
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STOMP [====>--------------------------] 16% at 257 it/s, elapsed: 27s, eta: 2m
STOMP [====>--------------------------] 16% at 255 it/s, elapsed: 27s, eta: 2m
STOMP [====>--------------------------] 16% at 256 it/s, elapsed: 27s, eta: 2m
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STOMP [====>--------------------------] 16% at 255 it/s, elapsed: 27s, eta: 2m
STOMP [====>--------------------------] 16% at 256 it/s, elapsed: 27s, eta: 2m
STOMP [====>--------------------------] 16% at 255 it/s, elapsed: 27s, eta: 2m
STOMP [====>--------------------------] 16% at 256 it/s, elapsed: 27s, eta: 2m
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STOMP [====>--------------------------] 16% at 256 it/s, elapsed: 27s, eta: 2m
STOMP [====>--------------------------] 16% at 255 it/s, elapsed: 27s, eta: 2m
STOMP [====>--------------------------] 16% at 256 it/s, elapsed: 27s, eta: 2m
STOMP [====>--------------------------] 16% at 255 it/s, elapsed: 27s, eta: 2m
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STOMP [====>--------------------------] 16% at 255 it/s, elapsed: 27s, eta: 2m
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STOMP [====>--------------------------] 16% at 255 it/s, elapsed: 27s, eta: 2m
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STOMP [====>--------------------------] 16% at 255 it/s, elapsed: 27s, eta: 2m
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STOMP [====>--------------------------] 16% at 256 it/s, elapsed: 28s, eta: 2m
STOMP [====>--------------------------] 16% at 257 it/s, elapsed: 28s, eta: 2m
STOMP [====>--------------------------] 16% at 256 it/s, elapsed: 28s, eta: 2m
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STOMP [====>--------------------------] 17% at 255 it/s, elapsed: 29s, eta: 2m
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tictac3 <- Sys.time()
Motif.discovery<-compute(gene_ts, 11) %>% motifs() %>% visualize()
Motif.discovery
NULL
tictac3 <- Sys.time() - tictac3
cat(paste("compute(), motifs(), visualize() finished in ", tictac3, "seconds (with plot!)\n\n"))
compute(), motifs(), visualize() finished in 15.3663511276245 seconds (with plot!)
## compute(), motifs(), visualize() finished in 0.0680019855499268 seconds (with plot!)
# Motif.discovery<-compute(gene_ts, 11) %>% motifs(3) %>% visualize()
# Motif.discovery
Look at the value of motifs what changes in visualization
# Motif.discovery<-compute(gene_ts, 11) %>% motifs(5) %>% visualize()
# Motif.discovery<-compute(gene_ts, 11) %>% motifs(6) %>% visualize()
# Motif.discovery<-compute(gene_ts, 11) %>% motifs(10) %>% visualize()
# Motif.discovery<-compute(gene_ts, 11) %>% motifs(20) %>% visualize()
# Motif.discovery<-compute(gene_ts, 11) %>% motifs(200) %>% visualize()
# Motif.discovery<-compute(gene_ts, 11) %>% motifs(50) %>% visualize()
# Motif.discovery<-compute(gene_ts, 11) %>% motifs(2) %>% visualize()
# Motif.discovery<-compute(gene_ts, 11) %>% motifs(20) %>% visualize()
After removing T1 which is 0 for all observations
library(factoextra)
eu_dist<-dist(Gen_1)
hclust<-hclust(eu_dist)
plot(hclust)
manhatan_dist<-dist(Gen_1, method="manhattan")
fviz_dist(manhatan_dist)
# Cluster analysis when T1 is removed and in original data
# 73% variance explained
k2<- kmeans(data.frame(Gen_1), centers = 3, nstart = 25)
fviz_cluster(k2,data=data.frame(Gen_1),label=NA, main="Before standardization")
k2.1<- kmeans(data.frame(Gen_1), centers = 4, nstart = 25)
fviz_cluster(k2.1,data=data.frame(Gen_1),label=NA, main="Before standardization")
# for k2.1
# save the index for genes in each cluster and visualize them
cluster_1.2<-which(k2.1$cluster==1)
cluster_2.2<-which(k2.1$cluster==2)
cluster_3.2<-which(k2.1$cluster==3)
cluster_4.2<-which(k2.1$cluster==4)
length(cluster_1.2)
[1] 1351
#
length(cluster_2.2)
[1] 504
#
length(cluster_3.2)
[1] 94
#
length(cluster_4.2)
[1] 1959
Plot_gene=function(M){
A=plot(M[1,],ylim=c(-3.5,5),xlab="Time",ylab="value",main="Gene expression over time-before",col="red",lwd=3,type="l")
for (i in 1:nrow(M)){
lines(M[i,],col="grey")
}
lines(M[1,],lwd=3,col="red")
print(A)
}
# get the cluster visualization in one
par(mfrow=c(1,4))
Plot_gene(Gen_1[cluster_1.2,-1])
NULL
Plot_gene=function(M){
A=plot(M[1,],ylim=c(-3.5,3),xlab="Time",ylab="value",main="Gene expression over time-before",col="red",lwd=3,type="l")
for (i in 1:nrow(M)){
lines(M[i,],col="grey")
}
lines(M[1,],lwd=3,col="red")
print(A)
}
Plot_gene(Gen_1[cluster_2.2,-1])
NULL
Plot_gene=function(M){
A=plot(M[1,],ylim=c(-0.5,9),xlab="Time",ylab="value",main="Gene expression over time-before",col="red",lwd=3,type="l")
for (i in 1:nrow(M)){
lines(M[i,],col="grey")
}
lines(M[1,],lwd=3,col="red")
print(A)
}
Plot_gene(Gen_1[cluster_3.2,-1])
NULL
Plot_gene=function(M){
A=plot(M[1,],ylim=c(-2,6),xlab="Time",ylab="value",main="Gene expression over time-before",col="red",lwd=3,type="l")
for (i in 1:nrow(M)){
lines(M[i,],col="grey")
}
lines(M[1,],lwd=3,col="red")
print(A)
}
Plot_gene(Gen_1[cluster_4.2,])
NULL
########################### After standardization ###############
k2.1<- kmeans(data.frame(Gen_standardized), centers = 4, nstart = 25)
fviz_cluster(k2.1,data=data.frame(Gen_1),label=NA, main="Before standardization")
#
# for k2.1
# save the index for genes in each cluster and visualize them
cluster_1.2<-which(k2.1$cluster==1)
cluster_2.2<-which(k2.1$cluster==2)
cluster_3.2<-which(k2.1$cluster==3)
cluster_4.2<-which(k2.1$cluster==4)
Plot_gene=function(M){
A=plot(M[1,],ylim=c(-2,3),xlab="Time",ylab="value",main="Gene expression over time-after",col="red",lwd=3,type="l")
for (i in 1:nrow(M)){
lines(M[i,],col="grey")
}
lines(M[1,],lwd=3,col="red")
print(A)
}
# get the cluster visualization in one
par(mfrow=c(1,4))
Plot_gene(Gen_standardized[cluster_1.2,])
NULL
Plot_gene=function(M){
A=plot(M[1,],ylim=c(-3,3),xlab="Time",ylab="value",main="Gene expression over time-after",col="red",lwd=3,type="l")
for (i in 1:nrow(M)){
lines(M[i,],col="grey")
}
lines(M[1,],lwd=3,col="red")
print(A)
}
Plot_gene(Gen_standardized[cluster_2.2,-1])
NULL
Plot_gene=function(M){
A=plot(M[1,],ylim=c(-2.2,3),xlab="Time",ylab="value",main="Gene expression over time-after",col="red",lwd=3,type="l")
for (i in 1:nrow(M)){
lines(M[i,],col="grey")
}
lines(M[1,],lwd=3,col="red")
print(A)
}
Plot_gene(Gen_standardized[cluster_3.2,])
NULL
Plot_gene=function(M){
A=plot(M[1,],ylim=c(-2.3,3),xlab="Time",ylab="value",main="Gene expression over time-after",col="red",lwd=3,type="l")
for (i in 1:nrow(M)){
lines(M[i,],col="grey")
}
lines(M[1,],lwd=3,col="red")
print(A)
}
Plot_gene(Gen_standardized[cluster_4.2,])
NULL
###################################
# after data are transformed-> standardized data
# 78% variance explained
k3<- kmeans(data.frame(Gen_standardized[,]), centers = 3, nstart = 25)
fviz_cluster(k3,data=data.frame(Gen_standardized[,]),label=NA,main="After standardization")
k4<- kmeans(data.frame(Gen_standardized[,]), centers = 4, nstart = 25)
fviz_cluster(k4,data=data.frame(Gen_standardized[,]),label=NA,main="After standardization")
# save the index for genes in each cluster and visualize them
cluster_1<-which(k3$cluster==1)
cluster_2<-which(k3$cluster==2)
cluster_3<-which(k3$cluster==3)
length(cluster_1)
[1] 1582
#[1] 1587
length(cluster_2)
[1] 1594
#[1] 727
length(cluster_3)
[1] 732
#[1] 1594
# get the cluster visualization in one
par(mfrow=c(1,3))
Plot_gene(Gen_1[cluster_1,])
NULL
Plot_gene(Gen_1[cluster_2,])
NULL
Plot_gene(Gen_1[cluster_3,])
NULL
Reference: https://www.r-bloggers.com/2019/10/cluster-multiple-time-series-using-k-means-2/
In standardized data we see a clear division of genes in clusters.
# for visualization purpose
time_names<-colnames(Gen_1)# save time names
colnames(Gen_standardized)<-time_names
View(Gen_standardized)
heatmap(Gen_standardized)
heatmap(Gen_1)
library(factoextra)
manhatan_dist<-dist(Gen_1, method="manhattan")
hclust<-hclust(manhatan_dist)
plot(hclust)
fviz_dist(manhatan_dist)
manhatan_dist<-dist(Gen_standardized, method="manhattan")
fviz_dist(manhatan_dist)
library(factoextra)
minkowski_dist<-dist(Gen_1, method="minkowski",p=2)
hclust<-hclust(minkowski_dist)
plot(hclust)
fviz_dist(minkowski_dist)
NA
NA
minkowski_dist<-dist(Gen_standardized, method="minkowski",p=2)
fviz_dist(minkowski_dist)
eucl_dist<-dist(Gen_standardized)
fviz_dist(eucl_dist)
eu_dist<-dist(Gen_standardized)
dist_m <- as.matrix(eu_dist)
#dis_matrix<-write.csv(dist_m, "dist_matrix_graph.csv") # if want to save the result in csv
dist_mi <- 1/dist_m # one over, as qgraph takes similarity matrices as input
library(qgraph)
Registered S3 method overwritten by 'htmlwidgets':
method from
print.htmlwidget tools:rstudio
#jpeg('example_forcedraw.jpg', width=1000, height=1000, unit='px')
qgraph(dist_mi[1:100,1:100], layout='spring', vsize=3)
Warning: Non-finite weights are omitted
#dev.off()
index<-which(dist_mi<=0.3)
dist_na<-dist_mi
dist_na[-index]="NA"
dim(dist_na)
[1] 3908 3908
dist_reduced<-na.omit(dist_na)
dim(dist_reduced)
[1] 3908 3908
qgraph(na.omit(dist_reduced), layout='spring', vsize=3)
Warning: Non-finite weights are omittedError in abs(E$weight) : non-numeric argument to mathematical function
qgraph(dist_mi[1:500,1:500], layout='spring', vsize=3)
Warning: Non-finite weights are omitted
It seems from the cluster analysis and graphs that there is a small world of genes which are clearly different from the other genes. And these genes are the ones which have changes behavior during the last time periods.
Q<-qgraph(dist_mi[3000:3900,3000:3900], layout='spring', vsize=3)
# Q
# From To Weight
# 1 --- 2 0.83
# 2 --- 3 0.83
# 1 --- 4 0.86
# 2 --- 4 2.94
# 3 --- 4 0.86
reference: http://sachaepskamp.com/qgraph/examples
# # Correlations:
# Q <- qgraph(dist_mi[1:500,1:500], minimum = 0.1, cut = 0.9, vsize = 1.5, borders = TRUE)
# title("Distance Graph", line = 2.5)
# qgraph(Q, overlay = TRUE)
# title("distances", line = 2.5)
#qgraph(dist_mi, layout='spring', vsize=3)
reference: https://www.datanovia.com/en/blog/easy-correlation-matrix-analysis-in-r-using-corrr-package/
library(corrr)
res.cor <- correlate(Gen_1)# between time moments
Correlation computed with
• Method: 'pearson'
• Missing treated using: 'pairwise.complete.obs'
res.cor
res.cor %>% network_plot()# T2 is not correlated because in almost all genes this is the last moment of time observed and values retur to 0
res.cor <- correlate(Gen_1[,-11])# between time moments
Correlation computed with
• Method: 'pearson'
• Missing treated using: 'pairwise.complete.obs'
res.cor %>% network_plot(min_cor=0.1,colours = c("red", "white", "darkblue"))
From this graph we are understanding that the last moment of time are important for gene evolution they are highly positively correlated.This may be used as a reasoning to classify our genes based on their evolution and changes over intervals of time. Moments of time from 10.5 h to 31.5h have high correlations and then a drop from this set to Cyst. So, may we consider moments of time10.5 to 31.5 for possible clusters?
Reference: https://stackoverflow.com/questions/49500668/calculating-a-distance-matrix-by-dtw
library(dtw)
Loading required package: proxy
Attaching package: ‘proxy’
The following objects are masked from ‘package:stats’:
as.dist, dist
The following object is masked from ‘package:base’:
as.matrix
Loaded dtw v1.23-1. See ?dtw for help, citation("dtw") for use in publication.
library(dtwclust)
dtwclust:
Setting random number generator to L'Ecuyer-CMRG (see RNGkind()).
To read the included vignettes type: browseVignettes("dtwclust").
See news(package = "dtwclust") after package updates.
M<-matrix(c(2,3,4,3,2,1,2,3,4,3,2,1,1,9,9,9,0,0,0,2,1,2,3,8),nrow=6)
M
[,1] [,2] [,3] [,4]
[1,] 2 2 1 0
[2,] 3 3 9 2
[3,] 4 4 9 1
[4,] 3 3 9 2
[5,] 2 2 0 3
[6,] 1 1 0 8
dtw_distmat <- proxy::dist(M, method = "dtw", upper = TRUE, diag = TRUE)
dtw_distmat
1 2 3 4 5 6
1 0 12 14 12 4 10
2 12 0 5 0 12 17
3 14 5 0 5 16 22
4 12 0 5 0 12 17
5 4 12 16 12 0 11
6 10 17 22 17 11 0
#suppressPackageStartupMessages(library(dtw))
Data<-as.matrix(Gen_standardized)
dim(Data)
[1] 3908 11
dtw_distmat <- proxy::dist(Data, method = "dtw", upper = TRUE, diag = TRUE)
head(dtw_distmat,10)
Error in .Primitive("[")(x, 1:6, , drop = FALSE) :
incorrect number of dimensions
library(dtwclust)
View(gen_T1_T2_transp)
dtw_cluster2 = tsclust(gen_T1_T2_transp, type="partitional",k=6,preproc = zscore,distance="dtw_basic",centroid = "pam",trace=T)
Precomputing distance matrix...
Iteration 1: Changes / Distsum = 11 / 5580.749
Iteration 2: Changes / Distsum = 0 / 5571.213
Elapsed time is 2.92 seconds.
plot(dtw_cluster2)
dtw_cluster2@cluster
[1] 6 6 6 5 5 2 1 4 3 2 6
dtw_cluster1 = tsclust(gen_T1_T2_transp, type="partitional",k=3,preproc = zscore,distance="dtw_basic",centroid = "pam",trace=T)
Precomputing distance matrix...
Iteration 1: Changes / Distsum = 11 / 15965.39
Iteration 2: Changes / Distsum = 3 / 13206.46
Iteration 3: Changes / Distsum = 1 / 12327.11
Iteration 4: Changes / Distsum = 1 / 12098.35
Iteration 5: Changes / Distsum = 0 / 12098.35
Elapsed time is 2.9 seconds.
plot(dtw_cluster1)
plot(dtw_cluster1,type = "centroid")
plot(dtw_cluster1,type = "sc")
library(dtw);
alignment<-dtw(Gen_standardized[1,],Gen_standardized[2,],keep=TRUE)
Refrence: https://dynamictimewarping.github.io/r/
## Display the warping curve, i.e. the alignment curve
plot(alignment,type="threeway",col="red",lwd=3)
## Align and plot with the Rabiner-Juang type VI-c unsmoothed recursion
plot(dtw(Gen_standardized[1,],Gen_standardized[2,],keep=TRUE,step=rabinerJuangStepPattern(6,"c")),type="twoway",offset=-2,lwd=3,main="Gene 1 and Gene 2");
## See the recursion relation, as formula and diagram
rabinerJuangStepPattern(6,"c")
Step pattern recursion:
g[i,j] = min(
g[i-3,j-2] + d[i-2,j-1] + d[i-1,j ] + d[i ,j ] ,
g[i-1,j-1] + d[i ,j ] ,
g[i-2,j-3] + d[i-1,j-2] + d[i ,j-1] + 0 * d[i ,j ] ,
)
Normalization hint: N
plot(rabinerJuangStepPattern(6,"c"),lwd=3)
# sequence and sequence 6
alignment<-dtw(Gen_standardized[1,],Gen_standardized[6,],keep=TRUE)
## Display the warping curve, i.e. the alignment curve
plot(alignment,type="threeway",col="red",lwd=3)
## Align and plot with the Rabiner-Juang type VI-c unsmoothed recursion
plot(dtw(Gen_standardized[1,],Gen_standardized[6,],keep=TRUE,step=rabinerJuangStepPattern(6,"c")),type="twoway",offset=-2,lwd=3,main="Gene 1 and Gene 6");
## See the recursion relation, as formula and diagram
rabinerJuangStepPattern(6,"c")
Step pattern recursion:
g[i,j] = min(
g[i-3,j-2] + d[i-2,j-1] + d[i-1,j ] + d[i ,j ] ,
g[i-1,j-1] + d[i ,j ] ,
g[i-2,j-3] + d[i-1,j-2] + d[i ,j-1] + 0 * d[i ,j ] ,
)
Normalization hint: N
plot(rabinerJuangStepPattern(6,"c"),lwd=3)
# sequence 1 and sequence 3000
alignment<-dtw(Gen_standardized[1,],Gen_standardized[3000,],keep=TRUE)
## Display the warping curve, i.e. the alignment curve
plot(alignment,type="threeway",col="red",lwd=3)
## Align and plot with the Rabiner-Juang type VI-c unsmoothed recursion
plot(dtw(Gen_standardized[1,],Gen_standardized[3000,],keep=TRUE,step=rabinerJuangStepPattern(6,"c")),type="twoway",offset=-2,lwd=3,main="Gene 1 and Gene 3000");
## See the recursion relation, as formula and diagram
rabinerJuangStepPattern(6,"c")
Step pattern recursion:
g[i,j] = min(
g[i-3,j-2] + d[i-2,j-1] + d[i-1,j ] + d[i ,j ] ,
g[i-1,j-1] + d[i ,j ] ,
g[i-2,j-3] + d[i-1,j-2] + d[i ,j-1] + 0 * d[i ,j ] ,
)
Normalization hint: N
plot(rabinerJuangStepPattern(6,"c"),lwd=3)
To use this function, you would simply need to pass in the matrix of
gene expression time series as an argument, and the function will return
a matrix of DTW distances. For example, if your matrix of gene
expression time series is called geneMatrix, you could use
the following code to calculate the DTW distances:
calculateDTW <- function(geneMatrix) {
# Calculate the number of genes in the matrix
nGenes <- nrow(geneMatrix)
# Create an empty matrix to store the DTW distances
dtwMatrix <- matrix(0, nrow = nGenes, ncol = nGenes)
# Loop through each pair of genes
for (i in 1:(nGenes-1)) {
for (j in (i+1):nGenes) {
# Calculate the DTW distance between the two gene expression time series
dtwMatrix[i,j] <- dtw(geneMatrix[i,], geneMatrix[j,], keep=T)$distance
dtwMatrix[j,i] <- dtwMatrix[i,j]
}
}
return(dtwMatrix)
}
dtwMatrix[i,j] <- dtw(geneMatrix[i,], geneMatrix[j,], keep=T)$distance
dtwMatrix[j,i] <- dtwMatrix[i,j]
#Example
A <- matrix(c(1,2,3,4,5,3,4,5,4,3,6,7,8,7,6,5,4,5,9,34,34,21,32,43,5,4,32,12,12,23,45,54), nrow=4, byrow=TRUE)
# Calculate the DTW distance matrix for the matrix A
dtwMatrix <- calculateDTW(A)
# Print the DTW distance matrix
print(dtwMatrix)
[,1] [,2] [,3] [,4]
[1,] 0 18 151 160
[2,] 18 0 154 161
[3,] 151 154 0 66
[4,] 160 161 66 0
This matrix shows the DTW distances between each pair of gene
expression time series in the matrix A. For example, the
distance between the first and second time series is 18, the distance
between the first and third time series is 151, and so on. You can use
this matrix to compute the clusters of the genes using a clustering
algorithm, as described in the previous response.
# Load the ggplot2 package
library(ggplot2)
# Calculate the DTW distance matrix for the matrix A
distanceMatrix <- calculateDTW(A)
# Use the k-means algorithm to cluster the genes
k <- 3 # Set the number of clusters to 3
clusters <- kmeans(distanceMatrix, k)
# Create a data frame with the gene names and cluster assignments
geneData <- data.frame(gene=rownames(dtwMatrix), cluster = clusters$cluster)
Error in data.frame(gene = rownames(dtwMatrix), cluster = clusters$cluster) :
arguments imply differing number of rows: 0, 4
This will create a scatter plot of the genes in the matrix
A, with the genes in each cluster being plotted in a
different color. This plot can be useful for visualizing the clusters
that were identified by the clustering algorithm, and can help you to
understand the underlying structure of the gene expression data.
# Load the required packages
library(tseries)
library(zoo)
library(stats)
library(fpc)
library(ggplot2)
# Define the matrix A
A <- matrix(c(1,2,3,4,5,3,4,5,4,3,6,7,8,7,6,5,4,5,9,34,34,21,32,43,5,4,32,12,12,23,45,54), nrow=4, byrow=TRUE)
# Calculate the DTW distance matrix for the matrix A
calculateDTW <- function(geneMatrix) {
# Calculate the number of genes in the matrix
nGenes <- nrow(geneMatrix)
# Create an empty matrix to store the DTW distances
dtwMatrix <- matrix(0, nrow = nGenes, ncol = nGenes)
# Loop through each pair of genes
for (i in 1:(nGenes-1)) {
for (j in (i+1):nGenes) {
# Calculate the DTW distance between the two gene expression time series
dtwMatrix[i,j] <- dtw(geneMatrix[i,], geneMatrix[j,], keep=T)$distance
dtwMatrix[j,i] <- dtwMatrix[i,j]
}
}
return(dtwMatrix)
}
distanceMatrix <- calculateDTW(A)
distanceMatrix
[,1] [,2] [,3] [,4]
[1,] 0 18 151 160
[2,] 18 0 154 161
[3,] 151 154 0 66
[4,] 160 161 66 0
# Use the k-means algorithm to cluster the genes
x
Error: object 'x' not found